Fuzzy C-means Clustering Method Research Articles

A Hybrid Predicting Model for the Daily Photovoltaic Output Based on Fuzzy Clustering of Meteorological Data and Joint Algorithm of GAPS and RBF Neural Network

Photovoltaic (PV) output is greatly affected by meteorological factors. If it has no efficient meteorological factors, the prediction accuracy for PV is a little low. Although the Radial Basis Function (RBF) network is already widely utilized in photovoltaic prediction, its prediction error is too large. An algorithm for forecasting the evaluation of the short-term PV output based on fuzzy clustering of meteorological data and a joint algorithm of the Genetic Algorithm Programming System (GAPS) and Radial Basis Function (RBF) is proposed in this paper to increase the prediction accuracy. Selecting the three main types of meteorological data, including atmospheric turbidity, relative humidity, and solar irradiance, as clustering feature vectors of the cluster class and clustering that historical PV outputting data into three groups by an improved fuzzy c-means clustering (IFCM) method are significant in this study. Finally, this research implemented the computational simulation for a real case. Its results show that the proposed model and algorithm work well and can reduce the dimension of the model and improve the prediction accuracy.

Prediction of the bond strength of FRP-to-concrete under direct tension by ACO-based ANFIS approach

In this study, a hybrid model integrating the ant colony optimization (ACO) algorithm and fuzzy c-means (FCM) clustering method into the adaptive neuro-fuzzy inference system (ANFIS) was proposed to predict the bond strength between fibre-reinforced polymer (FRP) sheets and concrete surface under direct tension. Eight parameters including the compressive strength of concrete, maximum aggregate size, tensile strength of FRP, thickness of FRP, elastic modulus of FRP, adhesive tensile strength, length of FRP and width of FRP are employed as the inputs, and the bond strength is used as the output variable. A comparison was conducted between some existing empirical models and the proposed hybrid ACO-based ANFIS model. The results confirmed that the developed ACO-based ANFIS model exhibits greater accuracy than the other eleven models, with higher coefficient of determination (R2 = 0.97) and Nash-Sutcliffe efficiency index (NS = 0.97), and lower root mean squared error (RMSE = 1.29 kN), mean absolute error (MAE = 0.81 kN) and mean absolute relative error (MARE = 0.053), while according to the Akaike information criterion (AIC) index, the accuracy of this model lies in its considerable complexity compared to others.

Generating Flood Hazard Maps Based on an Innovative Spatial Interpolation Methodology for Precipitation

In this study, a new approach for rainfall spatial interpolation in the Luxembourgian case study is introduced. The method used here is based on a Fuzzy C-Means (FCM) clustering method. In a typical FCM procedure, there are a lot of available data and each data point belongs to a cluster, with a membership degree [0 1]. On the other hand, in our methodology, the center of clusters is determined first and then random data are generated around cluster centers. Therefore, this approach is called inverse FCM (i-FCM). In order to calibrate and validate the new spatial interpolation method, seven rain gauges in Luxembourg, Germany and France (three for calibration and four for validation) with more than 10 years of measured data were used and consequently, the rainfall for ungauged locations was estimated. The results show that the i-FCM method can be applied with acceptable accuracy in validation rain gauges with values for R2 and RMSE of (0.94–0.98) and (9–14 mm), respectively, on a monthly time scale and (0.86–0.89) and (1.67–2 mm) on a daily time scale. In the following, the maximum daily rainfall return periods (10, 25, 50 and 100 years) were calculated using a two-parameter Weibull distribution. Finally, the LISFLOOD FP flood model was used to generate flood hazard maps in Dudelange, Luxembourg with the aim to demonstrate a practical application of the estimated local rainfall return periods in an urban area.

Improved fuzzy C-means clustering algorithm based on t-SNE for terahertz spectral recognition

Terahertz (THz) waves characterized by low energy, instantaneity and proficiency in spectral analysis, have promising future in material identification. The main dimensionality reduction methods of Terahertz-based material identification, including Principal Component Analysis (PCA), Local Preserving Projection (LPP) and Local Linear Embedding (LLE), are sensitive to the number of nearest neighbor samples and neglect difference among classes, thus making it difficult to design the subsequent clustering model or leading to incorrect clustering. The t-distributed Stochastic Neighbor Embedding (t-SNE), which regards the sample distribution in the high-dimension as a Gaussian distribution and the coordinates in the low-dimension as t-distribution, makes the distance of clusters with long distance longer and then relieve their congestion. Besides, in the traditional Fuzzy C-means (FCM) clustering methods, the initial clustering center is randomly determined, so the problem of local optimum is easy to appear, which is prone to cause wrong recognition. To solve the above problems, an improved FCM algorithm is proposed in this paper for Terahertz spectral recognition. Firstly, t-SNE method is used for dimensional reduction and for the selection of initial clustering center for a more accurate clustering effect. On this basis, classical FCM clustering is used to recognize different substances through Terahertz spectrum. The algorithm can not only relieve the congestion among classes in the process of clustering, but also reflect the distance there for an appropriate cluster center in samples. In order to verify the reliability of the proposed method, the Terahertz time-domain spectroscopy is used to detect three genetically modified cotton seeds of lumianyan28, lumianyan29 and lumianyan36 respectively, obtaining their time-domain spectral data. It is the proposed method, which is used to analyze the spectral data, that successfully distinguishes three different types of transgenic cotton seeds, with a total accuracy of 0.9668. The result shows that the clustering method proposed in the paper has a bright prospect in identifying the Terahertz spectrum of materials.

Implementation of Fuzzy C-Means for Clustering the Majelis Ulama Indonesia (MUI) Fatwa Documents

Since the Indonesian Ulema Council (MUI) was established in 1975 until now, this institution has produced 201 edicts covering various fields. Text mining is one of the techniques used to collect data hidden from data that form text. One method of extracting text is Clustering. The present study implements the Fuzzy C-Means Clustering method in MUI fatwa documents to classify existing fatwas based on the similarity of the issues discussed. Silhouette Coefficient is used to analyze the resulting clusters, with the best value of 0.0982 with 10 clusters grouping. Classify fatwas based on the similarity of the issues discussed can make it easier and faster in the search for an Islamic law in Indonesia.

Multi-Model Approach and Fuzzy Clustering for Mammogram Tumor to Improve Accuracy

Breast Cancer is one of the most common diseases among women which seriously affect health and threat to life. Presently, mammography is an uttermost important criterion for diagnosing breast cancer. In this work, image of breast cancer mass detection in mammograms with 1024×1024 pixels is used as dataset. This work investigates the performance of various approaches on classification techniques. Overall support vector machine (SVM) performs better in terms of log-loss and classification accuracy rate than other underlying models. Therefore, further extensions (i.e., multi-model ensembles method, Fuzzy c-means (FCM) clustering and SVM combination method, and FCM clustering based SVM model) and comparison with SVM have been performed in this work. The segmentation by FCM clustering technique allows one piece of data to belong in two or more clusters. The additional parts are due to the segmented image to enhance the tumor-shape. Simulation provides the accuracy and the area under the ROC curve for mini-MIAS are 91.39% and 0.964 respectively which give the confirmation of the effectiveness of the proposed algorithm (FCM-based SVM). This method increases the classification accuracy in the case of a malignant tumor. The simulation is based on R-software.

Multi-Task Fusion Deep Learning Model for Short-Term Intersection Operation Performance Forecasting

Urban road intersection bottleneck has become an important factor in causing traffic delay and restricting traffic efficiency. It is essential to explore the prediction of the operating performance at intersections in real-time and formulate corresponding strategies to alleviate intersection delay. However, because of the sophisticated intersection traffic condition, it is difficult to capture the intersection traffic Spatio-temporal features by the traditional data and prediction methods. The development of big data technology and the deep learning model provides us a good chance to address this challenge. Therefore, this paper proposes a multi-task fusion deep learning (MFDL) model based on massive floating car data to effectively predict the passing time and speed at intersections over different estimation time granularity. Moreover, the grid model and the fuzzy C-means (FCM) clustering method are developed to identify the intersection area and derive a set of key Spatio-temporal traffic parameters from floating car data. In order to validate the effectiveness of the proposed model, the floating car data from ten intersections of Beijing with a sampling rate of 3s are adopted for the training and test process. The experiment result shows that the MFDL model enables us to capture the Spatio-temporal and topology feature of the traffic state efficiently. Compared with the traditional prediction method, the proposed model has the best prediction performance. The interplay between these two targeted prediction variables can significantly improve prediction accuracy and efficiency. Thereby, this method predicts the intersection operation performance in real-time and can provide valuable insights for traffic managers to improve the intersection’s operation efficiency.

Spatial prediction of human brucellosis (HB) using a GIS-based adaptive neuro-fuzzy inference system (ANFIS)

ObjectiveThis study pursues three main objectives: 1) exploring the spatial distribution patterns of human brucellosis (HB); 2) identifying parameters affecting the disease spread; and 3) modeling and predicting the spatial distribution of HB cases in 2012–2016 and 2017–2018, respectively, in rural districts of Mazandaran province, Iran. MethodsWe collected data on the disease incidence, demography, ecology, climate, topography, and vegetation. Using the Global Moran’s I statistic, we measured spatial autocorrelation between log (number of HB cases). We applied the Getis-Ord Gi* statistic to identify areas with high and low risk of the disease. To investigate the relationships between the factors affecting the incidence of HB as input variables together and the factors with the log (number of HB cases) as an output variable, we used the statistical linear regression model and the Pearson correlation coefficient. Then, we implemented a GIS-based adaptive neuro-fuzzy inference system (ANFIS) with two subtractive clustering and fuzzy c-means (FCM) clustering methods to model and predict the spatial distribution of HB. ResultsGlobal Moran's I spatial autocorrelation analysis indicated that the type of HB distribution is clustered in all years except 2014 and 2017, which are random. According to the Getis-Ord Gi* analysis, the location of the hot spots varied during 2012–2018. In 2012 and 2013, most of the hot spots were seen in the west of the province. While in 2018, they were mostly concentrated in the eastern regions of the province. The linear regression model indicated that the parameters affecting the incidence of HB are independent of each other and can explain only 25.3% of the total changes in the log (number of HB cases). The results of the Pearson correlation coefficient showed that there were positive relationships between vegetation, log (population), and the number of sheep and cattle (p-value < 0.05). The above-mentioned factors had the strongest positive correlation with the log (number of HB cases) (p-value < 0.01). These results may be due to the fact that vegetation regions are suitable for livestock grazing, attracting large crowds of people. Therefore, this will increase HB cases. We compared the results of subtractive clustering and FCM clustering methods by evaluation criteria (e.g., linear correlation coefficient (LCC) and mean absolute error (MAE)) in two phases of development and assessment of the ANFIS model. In the assessment phase, we predicted the spatial distribution of log (number of HB cases) in 2017 and 2018 by subtractive clustering (R2 = 0.699, LCC or R = 0.692, MAE = 0.509, MSE = 0.455) and by FCM clustering (R2 = 0.704, LCC or R = 0.697, MAE = 0.512, MSE = 0.448) that showed FCM clustering outperformed the subtractive clustering. ConclusionThe findings may have important implications for public health. The emergence of the hot spots in the east of the province can be a warning to the health system. Health authorities can use the findings of this study to predict the spread of HB and perform HB prevention programs. They can also investigate the factors affecting the prevalence of the disease, identify high-risk areas, and ultimately allocate resources to high-risk regions.

Distance Functions Study in Fuzzy C-Means Core and Reduct Clustering

Fuzzy C-Means is a distance-based clustering process which applied by fuzzy logic concept. Clustering process worked in linear to the iteration process to minimizing the objective function. The objective function is an addition of the multiplication between the coordinates distance towards their closest cluster centroid and their membership degree. The more the iteration process, the objective function should get lower and lower. The objective of this research is to observe whether the distances which usually applied are able to fulfill the aforementioned hypothesis for determining the most suitable distance for Fuzzy C-Means clustering application. Few distance function was applied in the same dataset. 5 standard datasets and 2 random datasets were used to test the fuzzy c-means clustering performance with the 7 different distance function. Accuracy, purity, and Rand Index also applied to measure the quality of the resulted cluster. The observation result depicted that the distance function which resulted in the best quality of clusters are Euclidean, Average, Manhattan, Minkowski, Minkowski-Chebisev, and Canberra distance. These 6 distances were able to fulfill the basic hypothesis of the objective function behavior on Fuzzy C-Means Clustering method. The only distance who were not able to fulfill the basic hypothesis is Chebisev distance.

Fuzzy clustering and distributed model for streamflow estimation in ungauged watersheds

This paper proposes a regionalization method for streamflow prediction in ungauged watersheds in the 7461 km2 area above the Gharehsoo Hydrometry Station in the Ardabil Province, in the north of Iran. First, the Fuzzy c-means clustering method (FCM) was used to divide 46 gauged (19) and ungauged (27) watersheds into homogenous groups based on a variety of topographical and climatic factors. After identifying the homogenous watersheds, the Soil and Water Assessment Tool (SWAT) was calibrated and validated using data from the gauged watersheds in each group. The calibrated parameters were then tested in another gauged watershed that we considered as a pseudo ungauged watershed in each group. Values of R-Squared and Nash–Sutcliffe efficiency (NSE) were both ≥ 0.70 during the calibration and validation phases; and ≥ 0.80 and ≥ 0.74, respectively, during the testing in the pseudo ungauged watersheds. Based on these metrics, the validated regional models demonstrated a satisfactory result for predicting streamflow in the ungauged watersheds within each group. These models are important for managing stream quantity and quality in the intensive agriculture study area.

Fuzzy C-Means (FCM) Clustering with Probabilistic Neural Network (PNN) Model for Detection and Classification of Rice Plant Diseases in Internet of Things-Cloud Centric Precision Agriculture

Presently, the field of Internet of Things (loT) has been employed in diverse applications like Smart Grid, Surveillance, Smart homes, and so on. Precision Agriculture is a concept of farm management which makes use of IoT and networking concepts to improve the crop health. Recognition of diseases from the plant images is an active research topic which makes use of machine learning (ML) approaches. This paper introduces an effective rice plant disease identification and classification model to identify the type of disease from infected rice plants. The proposed method aims to detect three rice plant diseases such as Bacterial leaf blight, Brown spot, and Leaf smut. The proposed method involves a set of different processes namely image acquisition, preprocessing, segmentation, feature extraction and classification. At the earlier stage, IoT devices will be used to capture the image and stores it with a cloud server, which executes the classification process. In the cloud, the rice plant images under preprocessing to improvise the quality of the image. Then, fuzzy c-means (FCM) clustering method is utilized for the segmentation of disease portion from a leaf image. Afterwards, feature extraction takes place under three kinds namely color, shape, and texture. Finally, probabilistic neural network (PNN) is applied for multi-class classification. A detailed experimental analysis ensured the effective classification performance of the proposed method under all the test images applied.

False Financial Statement Identification Based on Fuzzy C-Means Algorithm

Financial accountants falsify financial statements by means of financial techniques such as financial practices and financial standards, and when compared with conventional financial data, it is found that the falsified financial data often lack correlation or even contradict each other in terms of financial data indicators. At the same time, there are also inherent differences in reporting patterns from conventional financial data, but these differences are difficult to test manually. In this paper, the fuzzy C-means (FCM) clustering method is used to amplify these differences and thus identify false financial statements. In the proposed algorithm, firstly, the normalization constraint of the FCM clustering algorithm on the sum of affiliation of individual samples is relaxed to the constraint on the sum of affiliation of all samples, thus reducing the sensitivity of the algorithm to noise and isolated points; secondly, a new affiliation correction method is proposed to address the problem that the difference in affiliation is too large after the relaxation of the constraint. In the discussion of this paper, most of the information comes from the annual reports of companies, administrative penalty decisions of the Securities Regulatory Commission, and some information comes from research reports made by securities institutions, which are limited sources of information. The proposed method can correct the affiliation to a reasonable range, effectively avoiding the problem that some samples have too much affiliation and become a class of their own and also avoiding the problem that it is difficult to choose the termination threshold of the algorithm iteration due to too little affiliation, and can ensure that the constraint on the sum of affiliation of all samples is always satisfied during the iteration of the algorithm. The method has the characteristics of high recognition accuracy and has the significance of theoretical method innovation.

RecPOID: POI Recommendation with Friendship Aware and Deep CNN

In location-based social networks (LBSNs), exploit several key features of points-of-interest (POIs) and users on precise POI recommendation be significant. In this work, a novel POI recommendation pipeline based on the convolutional neural network named RecPOID is proposed, which can recommend an accurate sequence of top-k POIs and considers only the effect of the most similar pattern friendship rather than all user’s friendship. We use the fuzzy c-mean clustering method to find the similarity. Temporal and spatial features of similar friends are fed to our Deep CNN model. The 10-layer convolutional neural network can predict longitude and latitude and the Id of the next proper locations; after that, based on the shortest time distance from a similar pattern’s friendship, select the smallest distance locations. The proposed structure uses six features, including user’s ID, month, day, hour, minute, and second of visiting time by each user as inputs. RecPOID based on two accessible LBSNs datasets is evaluated. Experimental outcomes illustrate considering most similar friendship could improve the accuracy of recommendations and the proposed RecPOID for POI recommendation outperforms state-of-the-art approaches.

Partial-Beam Non-Orthogonal Multiple Access (PB-NOMA) With Fuzzy Clustering

This letter proposes a partial-beam non-orthogonal multiple access (PB-NOMA) system for millimeter wave (mmWave) channels. In the proposed system, the user terminals (UT)s partially belong to different user groups and power-domain partial NOMA (P-NOMA) technique is exploited in their allocated spectrum. While an individual beam serves the UTs in one group, the UTs in the same group, including the overlapped beam UTs, employ the P-NOMA scheme. Overlapped beam UTs are present when two near UTs are closely correlated and when they are equally belonging to two user groups. Fuzzy C-means (FCM) clustering method, which considers the channel correlation coefficient as a similarity measure, is proposed to group the users. Then, user pairing is performed based on the channel gain differences and fuzzy membership values. Simulation results reveal that the sum-rate performance of the proposed PB-NOMA scheme with fuzzy clustering is shown to be better than that of the conventional P-NOMA systems by adjusting overlapped beam ratio (OBR) and lower near UT overlap ratio (NOR) in a preferable group.

Estimation of soil organic carbon storage based on digital soil mapping technique.

Accurate spatial distribution information of soil properties would be helpful for improving the accuracy of soil organic carbon storage estimation. In this study, terrain factors were used as predictors, and the fuzzy C-means (FCM) clustering method was used to make digital soil prediction mapping for soil organic carbon content, soil bulk density, soil depth, and soil gravel content in Nanshan forest farm in Jiyuan City of Henan Province. Based on the digital mapping results, the prediction mapping of soil organic carbon density and the estimation of soil organic carbon storage were realized. The results showed that the average soil organic carbon density in the study area based on the digital soil mapping method was 4.24 kg·m-2, the mean error (ME), mean absolute error (MAE) and root mean square error (RMSE) of the prediction map were 0.08, 2.80 and 5.03 kg·m-2, respectively. The accuracy, stability and reliability of the prediction results were higher than the tradiation methods. The soil organic carbon storage in the study area was estimated to be 3.08×108 kg. Based on the digital soil mapping technology, only a small number of soil samples could be used to map and estimate the soil organic carbon density with high accuracy, which could characterize the spatial distribution characteristics of soil organic carbon density. This study provided a new way to estimate soil organic carbon storage, which would help to improve the accuracy and efficiency of soil organic carbon storage estimation.

Fuzzy Constrained Inversion of Magnetotelluric Data Using Guided Fuzzy C-Means Clustering

Magnetotelluric (MT) inversion algorithms are an essential tool in exploration geophysics because they provide us with resistivity models of the subsurface. Inverse problems are ill-posed and non-unique. Resistivity models produced by geologically unconstrained MT inversions are typically smooth, which means that the geological interpretations obtained based on the resistivity models will be ambiguous and uncertain. To solve the above problems, geophysicists have made many efforts over the years to introduce any prior information (such as geological structure information and physical property data) into models, which could help to better constrain the inverted model with the inversion algorithms. In the last few years, there has been renewed interest in machine learning techniques. Various machine learning methods have been applied to inversions. Using the fuzzy c-means (FCM) clustering method, geophysicists proposed new inversion algorithms that are capable of building petrophysical information into the inversion. Although the FCM has been used and advanced in previous work, geophysicists concluded that a priori information of the correct number and value of cluster centers is very important for a successful FCM inversion. In fact, it is difficult to obtain the appropriate clustering information in some geophysical survey areas. In this study, we present an effective way to build the petrophysical information for the MT inversion based on FCM clustering algorithm. When the actual petrophysical information is insufficient, considering the characteristics of MT data, we perform a one-dimensional blocky inversion that can clearly identify the interface with distinct electrical property contrast; then, we obtain the number and value of cluster centers from 1D blocky inversion for the further MT inversion with FCM clustering. The algorithm uses guided FCM clustering to improve the model within the iterative minimization during MT inversion. In our MT inversion method, we integrate the geophysical inversion and geological differentiation into a unified scheme, which interact and enhance each other. The resistivity models obtained from the inversion respect the geophysical and petrophysical data and are easy to geologically interpret. We tested the algorithm using two synthetic examples and a field data example.

Statistical analysis and fuzzy clustering model to predict limestone rock mass quality (Qsrm) and degree of karstification (D.K) using geophysical parameters (case study of some areas in western Iran)

Since rock mass classification is based on common Q classification system and its modified system for sedimentary rocks (Qsrm) requires direct access to the rock mass, indirect methods are usually used to predict Q and Qsrm. The most important methods are geophysical methods such as pressure wave velocity (Vp) and electrical resistivity (ER). Considering the characteristics of cavities and other important properties of limestones in Qsrm, this classification is a good measure of quality in these rocks. The degree of karstification (D.K), in addition to the Qsrm, is also an important parameter in these rocks. Therefore, in this study, in addition to primary statistical analysis, using fuzzy C-means clustering (FCM) method, a multi-input–multi-output model for indirect estimation of Qsrm and D.K is presented. In addition to Vp and ER, two other independent variables called DCE and Kp were used to indirect prediction of Qsrm and D.K. DCE is the difference in the error of calculating the experimental relationships in estimating Q and Qsrm using Vp and ER, and Kp represents the ratio of Vp in the field to its value in the laboratory (Kp = VPLab/VPField). The proposed model has the ability to delete any of the mentioned independent variables in the absence of sufficient data except Vp. R2, RMSE and VAF evaluation indicators are highly consistent with the FCM model performance and when all four independent variables are used in the input data, we will have the best efficiency of the model.

Prediction of human diseases using optimized clustering techniques

In this paper an attempt is made to study the unsupervised learning clustering algorithms such as K-Means, Agglomerative, and Fuzzy C-means Clustering methods. The Nature inspired metaheuristics algorithms such as Salp Swarm Optimization Algorithm (SSOA), Gray Wolf Optimization (GWO), Whale Optimization Algorithm (WOA), Moth-Flame Optimization Algorithm (MFO), BAT Optimization Algorithm (BAT), and Firefly Algorithm (FFA) are used to optimize the unsupervised algorithms for better results. Objective functions like Sum of Squared Errors and Total within-Cluster Variation are taken as objective function in the optimization algorithms. The datasets used in the paper are Heart, Liver and Appendicitis. It has been found that the K-Means have given better results compared to the other algorithms.

A Transfer Deep Generative Adversarial Network Model to Synthetic Brain CT Generation from MR Images

Background. The generation of medical images is to convert the existing medical images into one or more required medical images to reduce the time required for sample diagnosis and the radiation to the human body from multiple medical images taken. Therefore, the research on the generation of medical images has important clinical significance. At present, there are many methods in this field. For example, in the image generation process based on the fuzzy C‐means (FCM) clustering method, due to the unique clustering idea of FCM, the images generated by this method are uncertain of the attribution of certain organizations. This will cause the details of the image to be unclear, and the resulting image quality is not high. With the development of the generative adversarial network (GAN) model, many improved methods based on the deep GAN model were born. Pix2Pix is a GAN model based on UNet. The core idea of this method is to use paired two types of medical images for deep neural network fitting, thereby generating high‐quality images. The disadvantage is that the requirements for data are very strict, and the two types of medical images must be paired one by one. DualGAN model is a network model based on transfer learning. The model cuts the 3D image into multiple 2D slices, simulates each slice, and merges the generated results. The disadvantage is that every time an image is generated, bar‐shaped “shadows” will be generated in the three‐dimensional image. Method/Material. To solve the above problems and ensure the quality of image generation, this paper proposes a Dual3D&amp;PatchGAN model based on transfer learning. Since Dual3D&amp;PatchGAN is set based on transfer learning, there is no need for one‐to‐one paired data sets, only two types of medical image data sets are needed, which has important practical significance for applications. This model can eliminate the bar‐shaped “shadows” produced by DualGAN’s generated images and can also perform two‐way conversion of the two types of images. Results. From the multiple evaluation indicators of the experimental results, it can be analyzed that Dual3D&amp;PatchGAN is more suitable for the generation of medical images than other models, and its generation effect is better.

Implementation of Fuzzy C-Means (FCM) Clustering Based Camouflage Image Generation Algorithm

Camouflage plays a fundamental role in modern electrical confrontation. Two important elements of camouflage are camouflage colors and camouflage textures. Many methods were presented to extract the main colors of the background. However, there are few methods to extract the background textures at present. The traditional methods based on watershed segmentation or background contour segmentation are computationally complex and time-consuming, being difficult to meet the real-time requirements. In this paper, a camouflage generation algorithm based on rectangle blocks scrambling and Fuzzy C-Means (FCM) clustering method is proposed. The algorithm consists of three modules, namely (1) the rectangle blocks segmentation module, (2) the rectangle scrambling module, and (3) the extraction of background dominant colors. Firstly, the texture features of the background image are simulated by rectangle blocks segmentation and scrambling algorithm, which avoids the complex calculation process and the loss of textures information compared with traditional algorithms based on description operators for background textures extraction. Next, Fuzzy C-Means (FCM) method is used to extract the main colors of background image with high accuracy and fast speed. In addition, experiments show that the proposed algorithm reduces the computing time and presents better concealment effect by retaining the similar domain colors. Compared with the template traversal algorithm and the watershed segmentation algorithm, the proposed algorithm features reduced computing time by more than 50%, and an increased similarity between the generated texture and background texture to more than 90%.