团结水电站堆石坝石料“C”值对坝体稳定计算的影响

碾压后堆石体“C”值的存在是客观的，而且对堆石体的稳定安全系数反映又较为敏感，所以，建议在堆石坝体稳定分行中，在审慎研究的基础上．对“C”的采用上不应完全以零计，而应合理地留有一定安全度，又要适当采用以达到使工程既安全又经济合理的目的．

NEW SHADOWED C-MEANS CLUSTERING WITH FEATURE WEIGHTS

Partition-based clustering with weighted feature is developed in the framework of shadowed sets. The objects in the core and boundary regions, generated by shadowed sets-based clustering, have different impact on the prototype of each cluster. By integrating feature weights, a formula for weight calculation is introduced to the clustering algorithm. The selection of weight exponent is crucial for good result and the weights are updated iteratively with each partition of clusters. The convergence of the weighted algorithms is given, and the feasible cluster validity indices of data mining application are utilized. Experimental results on both synthetic and real-life numerical data with different feature weights demonstrate that the weighted algorithm is better than the other unweighted algorithms.

ALLIED FUZZY c-MEANS CLUSTERING MODEL

A novel model of fuzzy clustering, i.e. an allied fuzzy c means （AFCM） model is proposed based on the combination of advantages of fuzzy c means （FCM） and possibilistic c means （PCM） clustering. PCM is sensitive to initializations and often generates coincident clusters. AFCM overcomes this shortcoming and it is an ex tension of PCM. Membership and typicality values can be simultaneously produced in AFCM. Experimental re- suits show that noise data can be well processed, coincident clusters are avoided and clustering accuracy is better.

Fuzzy c-means text clustering based on topic concept sub-space

To improve the accuracy of text clustering, fuzzy c-means clustering based on topic concept sub-space （TCS2FCM） is introduced for classifying texts. Five evaluation functions are combined to extract key phrases. Concept phrases, as well as the descriptions of final clusters, are presented using WordNet origin from key phrases. Initial centers and membership matrix are the most important factors affecting clustering performance. Orthogonal concept topic sub-spaces are built with the topic concept phrases representing topics of the texts and the initialization of centers and the membership matrix depend on the concept vectors in sub-spaces. The results show that, different from random initialization of traditional fuzzy c-means clustering, the initialization related to text content contributions can improve clustering precision.

Robust edge detection based on stationary wavelet transform

By combining multiscale stationary wavelet analysis with fuzzy c-means, a robust edge detection algorithm is presented. Based on the translation invafiance built in multiscale stationary wavelet transform, components in different transformed sub-images corresponding to a pixel are employed to form a feature vector of the pixel. All the feature vectors are classified with unsupervised fuzzy c-means to segment the image, and then the edge pixels are checked out by the Canny detector. A series of images contaminated with different intensive Gaussian noises are used to test the novel algorithm. Experiments show that fairly precise edges can be checked out robustly from those images with fairly intensive noise by the proposed algorithm.

C-means-based ant colony algorithm for TSP

To solve the traveling salesman problem with the characteristics of clustering,a novel hybrid algorithm,the ant colony algorithm combined with the C-means algorithm,is presented.In order to improve the speed of convergence,the traveling salesman problem(TSP)data is specially clustered by the C-means algorithm,then,the result is processed by the ant colony algorithm to solve the problem.The proposed algorithm treats the C-means algorithm as a new search operator and adopts a kind of local searching strategy—2-opt,so as to improve the searching performance.Given the cluster number,the algorithm can obtain the preferable solving result.Compared with the three other algorithms—the ant colony algorithm,the genetic algorithm and the simulated annealing algorithm,the proposed algorithm can make the results converge to the global optimum faster and it has higher accuracy.The algorithm can also be extended to solve other correlative clustering combination optimization problems.Experimental results indicate the validity of the proposed algorithm.

Application of the Delaunay triangulation interpolation in distortion XRII image

To alleviate the distortion of XRII X-ray image intensifier images in the C-arm CT computer tomography imaging system an algorithm based on the Delaunay triangulation interpolation is proposed.First the causes of the phenomenon the classical correction algorithms and the Delaunay triangulation interpolation are analyzed.Then the algorithm procedure is explained using flow charts and illustrations. Finally experiments are described to demonstrate its effectiveness and feasibility. Experimental results demonstrate that the Delaunay triangulation interpolation can have the following effects.In the case of the same center the root mean square distances RMSD and standard deviation STD between the corrected image with Delaunay triangulation interpolation and the ideal image are 5.760 4 ×10 -14 and 5.354 2 ×10 -14 respectively.They increase to 1.790 3 2.388 8 2.338 8 and 1.262 0 1.268 1 1.202 6 after applying the quartic polynomial model L1 and model L2 to the distorted images respectively.The RMSDs and STDs between the corrected image with the Delaunay triangulation interpolation and the ideal image are 2.489 × 10 -13 and 2.449 8 ×10 -13 when their centers do not coincide. When the quartic polynomial model L1 and model L2 are applied to the distorted images they are 1.770 3 2.388 8 2.338 8 and 1.269 9 1.268 1 1.202 6 respectively.

Watershed classification by remote sensing indices： A fuzzy c-means clustering approach

Determining the relatively similar hydrological properties of the watersheds is very crucial in order to readily classify them for management practices such as flood and soil erosion control. This study aimed to identify homogeneous hydrological watersheds using remote sensing data in western Iran. To achieve this goal, remote sensing indices including SAVI, LAI, NDMI, NDVI and snow cover, were extracted from MODIS data over the period 2000 to 2015. Then, a fuzzy method was used to clustering the watersheds based on the extracted indices. A fuzzy c-mean(FCM) algorithm enabled to classify 38 watersheds in three homogeneous groups.The optimal number of clusters was determined through evaluation of partition coefficient, partition entropy function and trial and error. The results indicated three homogeneous regions identified by the fuzzy c-mean clustering and remote sensing product which are consistent with the variations of topography and climate of the study area. Inherently,the grouped watersheds have similar hydrological properties and are likely to need similar management considerations and measures.

Multimode Process Monitoring Based on Fuzzy C-means in Locality Preserving Projection Subspace

For complex industrial processes with multiple operational conditions, it is important to develop effective monitoring algorithms to ensure the safety of production processes. This paper proposes a novel monitoring strategy based on fuzzy C-means. The high dimensional historical data are transferred to a low dimensional subspace spanned by locality preserving projection. Then the scores in the novel subspace are classified into several overlapped clusters, each representing an operational mode. The distance statistics of each cluster are integrated though the membership values into a novel BID (Bayesian inference distance) monitoring index. The efficiency and effectiveness of the proposed method are validated though the Tennessee Eastman benchmark process.

Power interconnected system clustering with advanced fuzzy C-mean algorithm

An advanced fuzzy C-mean （FCM） algorithm was proposed for the efficient regional clustering of multi-nodes interconnected systems. Due to various locational prices and regional coherencies for each node and point, modified similarity measure was considered to gather nodes having similar characteristics. The similarity measure was needed to contain locafi0nal prices as well as regional coherency. In order to consider the two properties simultaneously, distance measure of fuzzy C-mean algorithm had to be modified. Regional clustering algorithm for interconnected power systems was designed based on the modified fuzzy C-mean algorithm. The proposed algorithm produces proper classification for the interconnected power system and the results are demonstrated in the example of IEEE 39-bus interconnected electricity system.

Adaptive WNN aerodynamic modeling based on subset KPCA feature extraction

In order to accurately describe the dynamic characteristics of flight vehicles through aerodynamic modeling, an adaptive wavelet neural network (AWNN) aerodynamic modeling method is proposed, based on subset kernel principal components analysis (SKPCA) feature extraction. Firstly, by fuzzy C-means clustering, some samples are selected from the training sample set to constitute a sample subset. Then, the obtained samples subset is used to execute SKPCA for extracting basic features of the training samples. Finally, using the extracted basic features, the AWNN aerodynamic model is established. The experimental results show that, in 50 times repetitive modeling, the modeling ability of the method proposed is better than that of other six methods. It only needs about half the modeling time of KPCA-AWNN under a close prediction accuracy, and can easily determine the model parameters. This enables it to be effective and feasible to construct the aerodynamic modeling for flight vehicles.

A model to determining the remaining useful life of rotating equipment,based on a new approach to determining state of degradation

Condition assessment is one of the most significant techniques of the equipment’s health management.Also,in PHM methodology cycle,which is a developed form of CBM,condition assessment is the most important step of this cycle.In this paper,the remaining useful life of the equipment is calculated using the combination of sensor information,determination of degradation state and forecasting the proposed health index.The combination of sensor information has been carried out using a new approach to determining the probabilities in the Dempster-Shafer combination rules and fuzzy c-means clustering method.Using the simulation and forecasting of extracted vibration-based health index by autoregressive Markov regime switching(ARMRS)method,final health state is determined and the remaining useful life(RUL)is estimated.In order to evaluate the model,sensor data provided by FEMTO-ST Institute have been used.

A product module mining method for PLM database

Modular technology can effectively support the rapid design of products, and it is one of the key technologies to realize mass customization design. With the application of product lifecycle management(PLM) system in enterprises, the product lifecycle data have been effectively managed. However, these data have not been fully utilized in module division, especially for complex machinery products. To solve this problem, a product module mining method for the PLM database is proposed to improve the effect of module division. Firstly, product data are extracted from the PLM database by data extraction algorithm. Then, data normalization and structure logical inspection are used to preprocess the extracted defective data. The preprocessed product data are analyzed and expressed in a matrix for module mining. Finally, the fuzzy c-means clustering(FCM) algorithm is used to generate product modules, which are stored in product module library after module marking and post-processing. The feasibility and effectiveness of the proposed method are verified by a case study of high pressure valve.

AN UNSUPERVISED CLASSIFICATION FOR FULLY POLARIMETRIC SAR DATA USING SPAN/H/α IHSL TRANSFORM AND THE FCM ALGORITHM

In this paper, the IHSL transform and the Fuzzy C-Means (FCM) segmentation algorithm are combined together to perform the unsupervised classification for fully polarimetric Synthetic Ap-erture Rader (SAR) data. We apply the IHSL colour transform to H/α/SPANspace to obtain a new space (RGB colour space) which has a uniform distinguishability among inner parameters and contains the whole polarimetric information in H/α/SPAN.Then the FCM algorithm is applied to this RGB space to finish the classification procedure. The main advantages of this method are that the parameters in the color space have similar interclass distinguishability, thus it can achieve a high performance in the pixel based segmentation algorithm, and since we can treat the parameters in the same way, the segmentation procedure can be simplified. The experiments show that it can provide an improved classification result compared with the method which uses the H/α/SPANspace di-rectly during the segmentation procedure.

Predictive value of multi-detector computed tomography for accurate diagnosis of serous cystadenoma:Radiologic-pathologic correlation

AIM： To identify multi-detector computed tomography （MDCT） features most predictive of serous cystadenomas （SCAs）, correlating with histopathology, and to study the impact of cyst size and MDCT technique on reader performance. METHODS： The MDCT scans of 164 patients with surgically verified pancreatic cystic lesions were reviewed by two readers to study the predictive value of various morphological features for establishing a diagnosis of SCAs. Accuracy in lesion characterization and reader confidence were correlated with lesion size （≤3 cm or 〉≥3 cm） and scanning protocols （dedicated vs routine）. RESULTS： 28/164 cysts （mean size, 39 mm; range, 8-92 mm） were diagnosed as SCA on pathology. The MDCT features predictive of diagnosis of SCA were microcystic appearance （22/28, 78.6%）, surface Iobulations （25/28, 89.3%） and central scar （9/28, 32.4%）. Stepwise logistic regression analysis showed that only microcystic appearance was significant for CT diagnosis of SCA （P = 0.0001）. The sensitivity, specificity and PPV of central scar and of combined microcystic appearance and Iobulations were 32.4%/100%/100% and 68%/100%/100%, respectively. The reader confidence was higher for lesions 〉 3 cm （P = 0.02） and for MDCT scans performed using thin collimation （1.25-2.5 mm） compared to routine 5 mm collimation exams （P 〉 0.05）. CONCLUSION： Central scar on MDCT is diagnostic of SCA but is seen in only one third of SCAs. Microcystic morphology is the most significant CT feature in diagnosis of SCA. A combination of microcystic appearance and surface Iobulations offers accuracy comparable to central scar with higher sensitivity.

Short-Term Wind Power Prediction Using Fuzzy Clustering and Support Vector Regression

A sustainable production of electricity is essential for low carbon green growth in South Korea. The generation of wind power as renewable energy has been rapidly growing around the world. Undoubtedly, wind energy is unlimited in potential. However due to its own intermittency and volatility, there are difficulties in the effective harvesting of wind energy and the integration of wind power into the current electric power grid. To cope with this, many works have been done for wind speed and power forecasting. In this paper, an SVR （support vector regression） using FCM （Fuzzy C-Means） is proposed for wind speed forecasting. This paper describes the design of an FCM based SVR to increase the prediction accuracy. Proposed model was compared with ordinary SVR model using balanced and unbalanced test data. Also, multi-step ahead forecasting result was compared. Kernel parameters in SVR are adaptively determined in order to improve forecasting accuracy. An illustrative example is given by using real-world wind farm dataset. According to the experimental results, it is shown that the proposed method provides better forecasts of wind power.

THRFuzzy:Tangential holoentropy-enabled rough fuzzy classifier to classification of evolving data streams

The rapid developments in the fields of telecommunication, sensor data, financial applications, analyzing of data streams, and so on, increase the rate of data arrival, among which the data mining technique is considered a vital process. The data analysis process consists of different tasks, among which the data stream classification approaches face more challenges than the other commonly used techniques. Even though the classification is a continuous process, it requires a design that can adapt the classification model so as to adjust the concept change or the boundary change between the classes. Hence, we design a novel fuzzy classifier known as THRFuzzy to classify new incoming data streams. Rough set theory along with tangential holoentropy function helps in the designing the dynamic classification model. The classification approach uses kernel fuzzy c-means(FCM) clustering for the generation of the rules and tangential holoentropy function to update the membership function. The performance of the proposed THRFuzzy method is verified using three datasets, namely skin segmentation, localization, and breast cancer datasets, and the evaluated metrics, accuracy and time, comparing its performance with HRFuzzy and adaptive k-NN classifiers. The experimental results conclude that THRFuzzy classifier shows better classification results providing a maximum accuracy consuming a minimal time than the existing classifiers.

Development of slope mass rating system using K-means and fuzzy c-means clustering algorithms

Classification systems such as Slope Mass Rating(SMR) are currently being used to undertake slope stability analysis. In SMR classification system, data is allocated to certain classes based on linguistic and experience-based criteria. In order to eliminate linguistic criteria resulted from experience-based judgments and account for uncertainties in determining class boundaries developed by SMR system,the system classification results were corrected using two clustering algorithms, namely K-means and fuzzy c-means(FCM), for the ratings obtained via continuous and discrete functions. By applying clustering algorithms in SMR classification system, no in-advance experience-based judgment was made on the number of extracted classes in this system, and it was only after all steps of the clustering algorithms were accomplished that new classification scheme was proposed for SMR system under different failure modes based on the ratings obtained via continuous and discrete functions. The results of this study showed that, engineers can achieve more reliable and objective evaluations over slope stability by using SMR system based on the ratings calculated via continuous and discrete functions.

New High Tc-Value Investigated in Superconducting System YBa2（C u3）1-xA gxO6.5x＋δ

A ceramic superconductor compound with composition YBa2（C u3）1-xA gxO6.5x＋δhas been prepared experimentally by solid state reaction from principal roots of high purity materials like Y203, BaO, CuO and Ag20. The study was concentrated on the effect of partial substitution of Ag with respect to Cu atoms by the ratios （x = 0, 1, 0.2, 0.3, 0.4 and 0.5） through different analysis and measurements. X-ray diffraction （XRD）, Scanning Electron Microscope （SEM） and Resistivity measurement is play an important role to show the improvement on high superconducting phase. It was found that the best substituted value of （x = 0.5） investigated a favor value of Tc equal nearly to （123 K）, due to more excess of Ag atoms in the structure. X-ray diffraction showed an orthorhombic structure related to high-To phase with high stability through diminishing some peaks related to low temperature superconducting phase, that was related to presence of multiphase derivative from YBCO-phase. SEM pictures give us more details on the surface morphology, grain and grain boundaries, it gives an indication on successful of sintering process, the last one is very important in forming superconducting phase.

Semi-supervised kernel FCM algorithm for remote sensing image classification

These problems of nonlinearity, fuzziness and few labeled data were rarely considered in traditional remote sensing image classification. A semi-supervised kernel fuzzy C-means （SSKFCM） algorithm is proposed to overcome these disadvantages of remote sensing image classification in this paper. The SSKFCM algorithm is achieved by introducing a kernel method and semi-supervised learning technique into the standard fuzzy C-means （FCM） algorithm. A set of Beijing-1 micro-satellite＇s multispectral images are adopted to be classified by several algorithms, such as FCM, kernel FCM （KFCM）, semi-supervised FCM （SSFCM） and SSKFCM. The classification results are estimated by corresponding indexes. The results indicate that the SSKFCM algorithm significantly improves the classification accuracy of remote sensing images compared with the others.