A novel radio-map establishment based on fuzzy clustering for hybrid K-Nearest Neighbor (KNN) and Artifi cial Neural Network (ANN) position algorithm in WLAN indoor environment is proposed. First of all, the Principal...A novel radio-map establishment based on fuzzy clustering for hybrid K-Nearest Neighbor (KNN) and Artifi cial Neural Network (ANN) position algorithm in WLAN indoor environment is proposed. First of all, the Principal Component Analysis (PCA) is utilized for the purpose of simplifying input dimensions of position estimation algorithm and saving storage cost for the establishment of radio-map. Then, reference points (RPs) calibrated in the off-line phase are divided into separate clusters by Fuzzy C-means clustering (FCM), and membership degrees (MDs) for different clusters are also allocated to each RPs. However, the singular RPs cased by the multi-path effect signifi cantly decreases the clustering performance. Therefore, a novel radio-map establishment method is presented based on the modifi cation of signal samples recorded at singular RPs by surface fitting. In the on-line phase, the region which the mobile terminal (MT) belongs to is estimated according to the MDs firstly. Then, in estimated small dimensional regions, MT's coordinates are calculated byKNN positioning method for efficiency purpose. However, for the regions including singular RPs, ANN method is utilized because ofits great pattern matching ability. Furthermore, compared with other typical indoor positioning methods, feasibility and effectiveness of this hybrid KNN/ANN method are also verified by the experimental results in static and tracking situations.展开更多
Wind farm power prediction is proposed based on adaptive feature weight entropy fuzzy clustering algorithm.According to the fuzzy clustering method,a large number of historical data of a wind farm in Inner Mongolia ar...Wind farm power prediction is proposed based on adaptive feature weight entropy fuzzy clustering algorithm.According to the fuzzy clustering method,a large number of historical data of a wind farm in Inner Mongolia are analyzed and classified.Model of adaptive entropy weight for clustering is built.Wind power prediction model based on adaptive entropy fuzzy clustering feature weights is built.Simulation results show that the proposed method could distinguish the abnormal data and forecast more accurately and compute fastly.展开更多
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...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.展开更多
Helicopter mathematical model mainly depends on design helicopter control system, flight simulator, and real time control simulation system. But it is difficult to establish a helicopter flight dynamics mathematical ...Helicopter mathematical model mainly depends on design helicopter control system, flight simulator, and real time control simulation system. But it is difficult to establish a helicopter flight dynamics mathematical model that has features such as rapidness, reliability and precision, because there is no unique and precise expression to some sophisticated phenomenon of helicopter. In this paper a fuzzy helicopter flight model is constructed based on the flight experimental data. The fuzzy model, which is identified by fuzzy inference, has characteristics of computed rapidness and high precision. In order to guarantee the precision of the identified fuzzy model, a new method is adopted to handle the conflict fuzzy rules. Additionally, using fuzzy clustering technology can effectively reduce the number of rules of fuzzy model, namely, the order of the fuzzy model. The simulation results indicate that the method of this paper is effective and feasible.展开更多
Spontaneous combustion is one of the greatest disasters in coal mines. Early recognition is important because it may be a potential inducement for other coalmine accidents. However, early recognition is difficult beca...Spontaneous combustion is one of the greatest disasters in coal mines. Early recognition is important because it may be a potential inducement for other coalmine accidents. However, early recognition is difficult because of the complexity of different coal mines. Fuzzy clustering has been proposed to incorporate the uncertainty of spontaneous combustion in coal mines and it can give a clear degree of classification of combustion. Because FCM clustering tends to become trapped in local minima, a new approach of fuzzy c-means clustering based on a genetic algorithm is there- fore proposed. Genetic algorithm is capable of locating optimal or near optimal solutions to difficult problems. It can be applied in many fields without first obtaining detailed knowledge about correlation. It is helpful in improving the effec- tiveness of fuzzy clustering in detecting spontaneous combustion. The effectiveness of the method is demonstrated by means of an experiment.展开更多
To deal with the nonlinear separable problem, the generalized noise clustering （GNC） algorithm is extended to a kernel generalized noise clustering （KGNC） model. Different from the fuzzy c-means （FCM） model and ...To deal with the nonlinear separable problem, the generalized noise clustering （GNC） algorithm is extended to a kernel generalized noise clustering （KGNC） model. Different from the fuzzy c-means （FCM） model and the GNC model which are based on Euclidean distance, the presented model is based on kernel-induced distance by using kernel method. By kernel method the input data are nonlinearly and implicitly mapped into a high-dimensional feature space, where the nonlinear pattern appears linear and the GNC algorithm is performed. It is unnecessary to calculate in high-dimensional feature space because the kernel function can do it just in input space. The effectiveness of the proposed algorithm is verified by experiments on three data sets. It is concluded that the KGNC algorithm has better clustering accuracy than FCM and GNC in clustering data sets containing noisy data.展开更多
For an airborne Iookdown radar, clutter power often changes dynamically about 80 dB with wide distributions as the platform moves. Therefore, clutter tracking techniques are required to guide the selection of const fa...For an airborne Iookdown radar, clutter power often changes dynamically about 80 dB with wide distributions as the platform moves. Therefore, clutter tracking techniques are required to guide the selection of const false alarm rate （CFAR） schemes. In this work, clutter tracking is done in image domain and an algorithm combining multifractal and fuzzy C-mean （FCM） cluster is proposed. The clutter with large dynamic distributions in power density is converted to steady distributions of multifractal exponents by the multifractal transformation with the optimum moment. Then, later, the main lobe and side lobe are tracked from the multifractal exponents by FCM clustering method.展开更多
In this research,an integrated classification method based on principal component analysis-simulated annealing genetic algorithm-fuzzy cluster means(PCA-SAGA-FCM)was proposed for the unsupervised classification of tig...In this research,an integrated classification method based on principal component analysis-simulated annealing genetic algorithm-fuzzy cluster means(PCA-SAGA-FCM)was proposed for the unsupervised classification of tight sandstone reservoirs which lack the prior information and core experiments.A variety of evaluation parameters were selected,including lithology characteristic parameters,poro-permeability quality characteristic parameters,engineering quality characteristic parameters,and pore structure characteristic parameters.The PCA was used to reduce the dimension of the evaluation pa-rameters,and the low-dimensional data was used as input.The unsupervised reservoir classification of tight sandstone reservoir was carried out by the SAGA-FCM,the characteristics of reservoir at different categories were analyzed and compared with the lithological profiles.The analysis results of numerical simulation and actual logging data show that:1)compared with FCM algorithm,SAGA-FCM has stronger stability and higher accuracy;2)the proposed method can cluster the reservoir flexibly and effectively according to the degree of membership;3)the results of reservoir integrated classification match well with the lithologic profle,which demonstrates the reliability of the classification method.展开更多
An approach for color image segmentation is proposed based on the contributions of color features to segmentation rather than the choice of a particular color space. The determination of effective color features depen...An approach for color image segmentation is proposed based on the contributions of color features to segmentation rather than the choice of a particular color space. The determination of effective color features depends on the analysis of various color features from each tested color image via the designed feature encoding. It is different from the pervious methods where self organized feature map (SOFM) is used for constructing the feature encoding so that the feature encoding can self organize the effective features for different color images. Fuzzy clustering is applied for the final segmentation when the well suited color features and the initial parameter are available. The proposed method has been applied in segmenting different types of color images and the experimental results show that it outperforms the classical clustering method. The study shows that the feature encoding approach offers great promise in automating and optimizing the segmentation of color images.展开更多
Lower groups of coal seams are presently being mined from water-inrush from coal floors in order to have safe production in the Yanzhou coal mining area. We need to evaluate the risk in the lower groups of coal seams ...Lower groups of coal seams are presently being mined from water-inrush from coal floors in order to have safe production in the Yanzhou coal mining area. We need to evaluate the risk in the lower groups of coal seams in mines. Based on a systematic collection of hydrogeological data and some data from mined working faces in these lower groups, we evaluated the factors affecting water-inrush from coal floors of the area by a method of dimensionless analysis. We obtained the order of the factors affecting water-inrush from coal floors and recalculated data on depths of destroyed floors by multiple linear regression analysis and obtained new empirical formulas. We also analyzed the water-inrush coefficient of mined working faces of the lower groups of coal seams and improved the evaluation standard of the water-inrush coefficient method. Finally, we made a comprehensive evaluation of water-inrush risks from coal floors by using the water-inrush coefficient method and a fuzzy clustering method. The evaluation results provide a solid foundation for preventing and controlling the damage caused by water of an Ordovician limestone aquifer in the lower group of coal seams in the mines of Yanzhou. It provides also important guidelines for lower groups of coal seams in other coal mines.展开更多
The room and pillar method is usually used to extract coal from shallowly buried seams with thin bedrock. This results in a very low production efficiency and in a low degree of extraction. In recent years short-wall ...The room and pillar method is usually used to extract coal from shallowly buried seams with thin bedrock. This results in a very low production efficiency and in a low degree of extraction. In recent years short-wall continuous mechanical mining has been extensively used in many situations except shallowly buried coal seams with thin bedrock. The principles governing movement of the overlying strata above the 2-2 coal seam were deduced from in-situ experience, laboratory data, calculations and computer simulations. The thicknesses of the bedrock in the Shendong Coal Field where the coal is shallowly buried are classified into 5 types: <10 m, 10–15 m, 15–25 m, 25–35 m and >35 m, which was done using fuzzy clustering results. A series of reasonable, relative parameters in each category have been calculated and analyzed. One proposed way to perform short-wall continuous mechanical mining in shallowly buried coal seams is given. This is significant for coal mines with similar geological conditions.展开更多
Heavy metal pollution brings extensive concerns since 1940s. In order to assess the heavy metal pollution on the farmland of Yanzhou coalfield, 216 soil samples and 54 combined samples were collected. Lead, cadmium, c...Heavy metal pollution brings extensive concerns since 1940s. In order to assess the heavy metal pollution on the farmland of Yanzhou coalfield, 216 soil samples and 54 combined samples were collected. Lead, cadmium, chromium, copper, zinc, and nickel contained in both topsoil and deep soil were analyzed using atomic absorbent spectrometry analyzer (AAS). Fuzzy clustering method was used in data processing. And fuzzy synthetic assessment was applied to assess the soil contamination by heavy metals. The result shows that Yanzhou coalfield has been polluted by the heavy metals to some extent.展开更多
By obtaining a feasible filter function,reconstructed images can be got with linear interpolation and liftered backprojection techniques.Considering the gray and spstial correlation neighbour informations of each pixe...By obtaining a feasible filter function,reconstructed images can be got with linear interpolation and liftered backprojection techniques.Considering the gray and spstial correlation neighbour informations of each pixel,a new supervised classification method is put forward for the reconstructed images,and an experiment with noise image is done,the result shows that the method is feasible and accurate compared with ideal phantoms.展开更多
To guarantee the accuracy and real-time of the 3D reconstruction method for outdoor scene,an algorithm based on region segmentation and matching was proposed.Firstly,on the basis of morphological gradient information,...To guarantee the accuracy and real-time of the 3D reconstruction method for outdoor scene,an algorithm based on region segmentation and matching was proposed.Firstly,on the basis of morphological gradient information,obtained by comparing color weight gradient images and proposing a multi-threshold segmentation,scene contour features were extracted by a watershed algorithm and a fuzzy c-means clustering algorithm.Secondly,to reduce the search area,increase the correct matching ratio and accelerate the matching speed,the region constraint was established according to a region＇s local position,area and gray characteristics,the edge pixel constraint was established according to the epipolar constraint and the continuity constraint.Finally,by using the stereo matching edge pixel pairs,their 3D coordinates were estimated according to the binocular stereo vision imaging model.Experimental results show that the proposed method can yield a high stereo matching ratio and reconstruct a 3D scene quickly and efficiently.展开更多
For a semi-supervised classification system, with the increase of the training samples number, the system needs to be continually updated. As the size of samples set is increasing, many unreliable samples will also be...For a semi-supervised classification system, with the increase of the training samples number, the system needs to be continually updated. As the size of samples set is increasing, many unreliable samples will also be increased. In this paper, we use fuzzy c-means （FCM） clustering to take out some samples that are useless, and extract the intersection between the original training set and the cluster after using FCM clustering. The intersection between every class and cluster is reliable samples which we are looking for. The experiment result demonstrates that the superiority of the proposed algorithm is remarkable.展开更多
基金supported by National High-Tech Research & Development Program of China (Grant No. 2008AA12Z305)
文摘A novel radio-map establishment based on fuzzy clustering for hybrid K-Nearest Neighbor (KNN) and Artifi cial Neural Network (ANN) position algorithm in WLAN indoor environment is proposed. First of all, the Principal Component Analysis (PCA) is utilized for the purpose of simplifying input dimensions of position estimation algorithm and saving storage cost for the establishment of radio-map. Then, reference points (RPs) calibrated in the off-line phase are divided into separate clusters by Fuzzy C-means clustering (FCM), and membership degrees (MDs) for different clusters are also allocated to each RPs. However, the singular RPs cased by the multi-path effect signifi cantly decreases the clustering performance. Therefore, a novel radio-map establishment method is presented based on the modifi cation of signal samples recorded at singular RPs by surface fitting. In the on-line phase, the region which the mobile terminal (MT) belongs to is estimated according to the MDs firstly. Then, in estimated small dimensional regions, MT's coordinates are calculated byKNN positioning method for efficiency purpose. However, for the regions including singular RPs, ANN method is utilized because ofits great pattern matching ability. Furthermore, compared with other typical indoor positioning methods, feasibility and effectiveness of this hybrid KNN/ANN method are also verified by the experimental results in static and tracking situations.
基金supported by the Natural Science Foundation of China under contact(61233007)
文摘Wind farm power prediction is proposed based on adaptive feature weight entropy fuzzy clustering algorithm.According to the fuzzy clustering method,a large number of historical data of a wind farm in Inner Mongolia are analyzed and classified.Model of adaptive entropy weight for clustering is built.Wind power prediction model based on adaptive entropy fuzzy clustering feature weights is built.Simulation results show that the proposed method could distinguish the abnormal data and forecast more accurately and compute fastly.
文摘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.
文摘Helicopter mathematical model mainly depends on design helicopter control system, flight simulator, and real time control simulation system. But it is difficult to establish a helicopter flight dynamics mathematical model that has features such as rapidness, reliability and precision, because there is no unique and precise expression to some sophisticated phenomenon of helicopter. In this paper a fuzzy helicopter flight model is constructed based on the flight experimental data. The fuzzy model, which is identified by fuzzy inference, has characteristics of computed rapidness and high precision. In order to guarantee the precision of the identified fuzzy model, a new method is adopted to handle the conflict fuzzy rules. Additionally, using fuzzy clustering technology can effectively reduce the number of rules of fuzzy model, namely, the order of the fuzzy model. The simulation results indicate that the method of this paper is effective and feasible.
基金Project 20050290010 supported by the Doctoral Foundation of Chinese Education Ministry
文摘Spontaneous combustion is one of the greatest disasters in coal mines. Early recognition is important because it may be a potential inducement for other coalmine accidents. However, early recognition is difficult because of the complexity of different coal mines. Fuzzy clustering has been proposed to incorporate the uncertainty of spontaneous combustion in coal mines and it can give a clear degree of classification of combustion. Because FCM clustering tends to become trapped in local minima, a new approach of fuzzy c-means clustering based on a genetic algorithm is there- fore proposed. Genetic algorithm is capable of locating optimal or near optimal solutions to difficult problems. It can be applied in many fields without first obtaining detailed knowledge about correlation. It is helpful in improving the effec- tiveness of fuzzy clustering in detecting spontaneous combustion. The effectiveness of the method is demonstrated by means of an experiment.
基金The 15th Plan National Defence Preven-tive Research Project (No.413030201)
文摘To deal with the nonlinear separable problem, the generalized noise clustering （GNC） algorithm is extended to a kernel generalized noise clustering （KGNC） model. Different from the fuzzy c-means （FCM） model and the GNC model which are based on Euclidean distance, the presented model is based on kernel-induced distance by using kernel method. By kernel method the input data are nonlinearly and implicitly mapped into a high-dimensional feature space, where the nonlinear pattern appears linear and the GNC algorithm is performed. It is unnecessary to calculate in high-dimensional feature space because the kernel function can do it just in input space. The effectiveness of the proposed algorithm is verified by experiments on three data sets. It is concluded that the KGNC algorithm has better clustering accuracy than FCM and GNC in clustering data sets containing noisy data.
基金This work was supported by the Aeronautical Science Foundation of China under Grand No. 04D52032.
文摘For an airborne Iookdown radar, clutter power often changes dynamically about 80 dB with wide distributions as the platform moves. Therefore, clutter tracking techniques are required to guide the selection of const false alarm rate （CFAR） schemes. In this work, clutter tracking is done in image domain and an algorithm combining multifractal and fuzzy C-mean （FCM） cluster is proposed. The clutter with large dynamic distributions in power density is converted to steady distributions of multifractal exponents by the multifractal transformation with the optimum moment. Then, later, the main lobe and side lobe are tracked from the multifractal exponents by FCM clustering method.
基金funded by the National Natural Science Foundation of China(42174131)the Strategic Cooperation Technology Projects of CNPC and CUPB(ZLZX2020-03).
文摘In this research,an integrated classification method based on principal component analysis-simulated annealing genetic algorithm-fuzzy cluster means(PCA-SAGA-FCM)was proposed for the unsupervised classification of tight sandstone reservoirs which lack the prior information and core experiments.A variety of evaluation parameters were selected,including lithology characteristic parameters,poro-permeability quality characteristic parameters,engineering quality characteristic parameters,and pore structure characteristic parameters.The PCA was used to reduce the dimension of the evaluation pa-rameters,and the low-dimensional data was used as input.The unsupervised reservoir classification of tight sandstone reservoir was carried out by the SAGA-FCM,the characteristics of reservoir at different categories were analyzed and compared with the lithological profiles.The analysis results of numerical simulation and actual logging data show that:1)compared with FCM algorithm,SAGA-FCM has stronger stability and higher accuracy;2)the proposed method can cluster the reservoir flexibly and effectively according to the degree of membership;3)the results of reservoir integrated classification match well with the lithologic profle,which demonstrates the reliability of the classification method.
文摘An approach for color image segmentation is proposed based on the contributions of color features to segmentation rather than the choice of a particular color space. The determination of effective color features depends on the analysis of various color features from each tested color image via the designed feature encoding. It is different from the pervious methods where self organized feature map (SOFM) is used for constructing the feature encoding so that the feature encoding can self organize the effective features for different color images. Fuzzy clustering is applied for the final segmentation when the well suited color features and the initial parameter are available. The proposed method has been applied in segmenting different types of color images and the experimental results show that it outperforms the classical clustering method. The study shows that the feature encoding approach offers great promise in automating and optimizing the segmentation of color images.
基金supports from the Natural Science Foundation of Shandong Province (No.Y2007F46)the Doctor Disciplines Special Scientific Research Foundation of Ministry of Education (No.20070424005)+1 种基金China Coal Industry Association Science and Technology Research Instructive Plan (No.MTKJ2009-290) the National Natural Science Foundation of China (No.50539080)
文摘Lower groups of coal seams are presently being mined from water-inrush from coal floors in order to have safe production in the Yanzhou coal mining area. We need to evaluate the risk in the lower groups of coal seams in mines. Based on a systematic collection of hydrogeological data and some data from mined working faces in these lower groups, we evaluated the factors affecting water-inrush from coal floors of the area by a method of dimensionless analysis. We obtained the order of the factors affecting water-inrush from coal floors and recalculated data on depths of destroyed floors by multiple linear regression analysis and obtained new empirical formulas. We also analyzed the water-inrush coefficient of mined working faces of the lower groups of coal seams and improved the evaluation standard of the water-inrush coefficient method. Finally, we made a comprehensive evaluation of water-inrush risks from coal floors by using the water-inrush coefficient method and a fuzzy clustering method. The evaluation results provide a solid foundation for preventing and controlling the damage caused by water of an Ordovician limestone aquifer in the lower group of coal seams in the mines of Yanzhou. It provides also important guidelines for lower groups of coal seams in other coal mines.
基金Projects NCET-05-0480 supported by the Program for New Century Excellent Talents in University07KF09 by the 2007 Research Fund of the State Key Laboratory of Coal Resources and Mine Safety
文摘The room and pillar method is usually used to extract coal from shallowly buried seams with thin bedrock. This results in a very low production efficiency and in a low degree of extraction. In recent years short-wall continuous mechanical mining has been extensively used in many situations except shallowly buried coal seams with thin bedrock. The principles governing movement of the overlying strata above the 2-2 coal seam were deduced from in-situ experience, laboratory data, calculations and computer simulations. The thicknesses of the bedrock in the Shendong Coal Field where the coal is shallowly buried are classified into 5 types: <10 m, 10–15 m, 15–25 m, 25–35 m and >35 m, which was done using fuzzy clustering results. A series of reasonable, relative parameters in each category have been calculated and analyzed. One proposed way to perform short-wall continuous mechanical mining in shallowly buried coal seams is given. This is significant for coal mines with similar geological conditions.
基金Project 30302408 supported by Land and Resource Ministry of China
文摘Heavy metal pollution brings extensive concerns since 1940s. In order to assess the heavy metal pollution on the farmland of Yanzhou coalfield, 216 soil samples and 54 combined samples were collected. Lead, cadmium, chromium, copper, zinc, and nickel contained in both topsoil and deep soil were analyzed using atomic absorbent spectrometry analyzer (AAS). Fuzzy clustering method was used in data processing. And fuzzy synthetic assessment was applied to assess the soil contamination by heavy metals. The result shows that Yanzhou coalfield has been polluted by the heavy metals to some extent.
文摘By obtaining a feasible filter function,reconstructed images can be got with linear interpolation and liftered backprojection techniques.Considering the gray and spstial correlation neighbour informations of each pixel,a new supervised classification method is put forward for the reconstructed images,and an experiment with noise image is done,the result shows that the method is feasible and accurate compared with ideal phantoms.
基金Supported by the Ministerial Level Advanced Research Foundation(40401060305)
文摘To guarantee the accuracy and real-time of the 3D reconstruction method for outdoor scene,an algorithm based on region segmentation and matching was proposed.Firstly,on the basis of morphological gradient information,obtained by comparing color weight gradient images and proposing a multi-threshold segmentation,scene contour features were extracted by a watershed algorithm and a fuzzy c-means clustering algorithm.Secondly,to reduce the search area,increase the correct matching ratio and accelerate the matching speed,the region constraint was established according to a region＇s local position,area and gray characteristics,the edge pixel constraint was established according to the epipolar constraint and the continuity constraint.Finally,by using the stereo matching edge pixel pairs,their 3D coordinates were estimated according to the binocular stereo vision imaging model.Experimental results show that the proposed method can yield a high stereo matching ratio and reconstruct a 3D scene quickly and efficiently.
基金supported by the National Natural Science Foundation under Grant No.61175055 and No.61105059support of research funds of Sichuan Key Laboratory of Intelligent Network Information Processing under Grant No.SGXZD1002-10Si chuan Key Technology Research and Development Program under Grant No.2012GZ0019 and No.2011FZ0051
文摘For a semi-supervised classification system, with the increase of the training samples number, the system needs to be continually updated. As the size of samples set is increasing, many unreliable samples will also be increased. In this paper, we use fuzzy c-means （FCM） clustering to take out some samples that are useless, and extract the intersection between the original training set and the cluster after using FCM clustering. The intersection between every class and cluster is reliable samples which we are looking for. The experiment result demonstrates that the superiority of the proposed algorithm is remarkable.
