Water quality assessment of lakes is important to determine functional zones of water use.Considering the fuzziness during the partitioning process for lake water quality in an arid area,a multiplex model of fuzzy clu...Water quality assessment of lakes is important to determine functional zones of water use.Considering the fuzziness during the partitioning process for lake water quality in an arid area,a multiplex model of fuzzy clustering with pattern recognition was developed by integrating transitive closure method,ISODATA algorithm in fuzzy clustering and fuzzy pattern recognition.The model was applied to partition the Ulansuhai Lake,a typical shallow lake in arid climate zone in the west part of Inner Mongolia,China and grade the condition of water quality divisions.The results showed that the partition well matched the real conditions of the lake,and the method has been proved accurate in the application.展开更多
To avoid the complexity of building mechanistic models by studying the inner nature of the object, a systematic method based on statistical pattern recognition is developed in order to estimate the product quality on-...To avoid the complexity of building mechanistic models by studying the inner nature of the object, a systematic method based on statistical pattern recognition is developed in order to estimate the product quality on-line. The mapping relationship between a feature space and a product quality space can be built by using regression analysis, and in applying clustering analysis the product quality space can be partitioned automatically. Eventually, estimating product quality on-line can be accomplished by sorting the mapped data in the partitioned quality space. A concrete problem is proposed which has a relatively small ratio of training data to input variables. By implementing the method mentioned above, a satisfying result has been achieved. Furthermore, the further question about choosing suitable mapping methods is briefly discussed.展开更多
In this paper, the characteristics of vibration signal of machinery in different running conditions are statistically analysed, and some moments of statistical distribution of signals are selected as the eigenvector t...In this paper, the characteristics of vibration signal of machinery in different running conditions are statistically analysed, and some moments of statistical distribution of signals are selected as the eigenvector to condense the state information. Here, we divide the states of machinery into two: 'good' and 'faulty', and the pattern recognition techniques are used to classify the running conditions of machinery. At the end of this paper, the authors present some test data, and from the results obtained, it's verified that the eigenvector selected is reliable and sensible to faults. And the results also show the effectiveness of classification rule.展开更多
A method about fault identification is proposed to solve the relationship among fault features of large rotating machinery, which is extremely complicated and nonlinear. This paper studies the rotor test-rig and the c...A method about fault identification is proposed to solve the relationship among fault features of large rotating machinery, which is extremely complicated and nonlinear. This paper studies the rotor test-rig and the clustering of data sets and fault pattern recognitions. The present method firstly maps the data from their original space to a high dimensional Kernel space which makes the highly nonlinear data in low-dimensional space become linearly separable in Kernel space. It highlights the differences among the features of the data set. Then fuzzy C-means （FCM） is conducted in the Kernel space. Each data is assigned to the nearest class by computing the distance to the clustering center. Finally, test set is used to judge the results. The convergence rate and clustering accuracy are better than traditional FCM. The study shows that the method is effective for the accuracy of pattern recognition on rotating machinery.展开更多
Structural Health Monitoring(SHM) suggests the use of machine learning algorithms with the aim of understanding specific behaviors in a structural system. This work introduces a pattern recognition methodology for ope...Structural Health Monitoring(SHM) suggests the use of machine learning algorithms with the aim of understanding specific behaviors in a structural system. This work introduces a pattern recognition methodology for operational condition clustering in a structure sample using the well known Density Based Spatial Clustering of Applications with Noise(DBSCAN) algorithm.The methodology was validated using a data set from an experiment with 32 Fiber Bragg Gratings bonded to an aluminum beam placed in cantilever and submitted to cyclic bending loads under 13 different operational conditions(pitch angles). Further, the computational cost and precision of the machine learning pipeline called FA + GA-DBSCAN(which employs a combination of machine learning techniques including factor analysis for dimensionality reduction and a genetic algorithm for the automatic selection of initial parameters of DBSCAN) was measured. The obtained results have shown a good performance, detecting 12 of 13 operational conditions, with an overall precision over 90%.展开更多
To recognize circular objects rapidly in satellite remote sensing imagery, an approach using their geometry properties is presented. The original image is segmented to be a binary one by one dimension maximum entropy ...To recognize circular objects rapidly in satellite remote sensing imagery, an approach using their geometry properties is presented. The original image is segmented to be a binary one by one dimension maximum entropy threshold algorithm and the binary image is labeled with an algorithm based on recursion technique. Then, shape parameters of all labeled regions are calculated and those regions with shape parameters satisfying certain conditions are recognized as circular objects. The algorithm is described in detail, and comparison experiments with the randomized Hough transformation （RHT） are also provided. The experimental results on synthetic images and real images show that the proposed method has the merits of fast recognition rate, high recognition efficiency and the ability of anti-noise and anti-jamming. In addition, the method performs well when some circular objects are little deformed and partly misshapen.展开更多
The discovery of gradual moving object clusters pattern from trajectory streams allows characterizing movement behavior in real time environment,which leverages new applications and services.Since the trajectory strea...The discovery of gradual moving object clusters pattern from trajectory streams allows characterizing movement behavior in real time environment,which leverages new applications and services.Since the trajectory streams is rapidly evolving,continuously created and cannot be stored indefinitely in memory,the existing approaches designed on static trajectory datasets are not suitable for discovering gradual moving object clusters pattern from trajectory streams.This paper proposes a novel algorithm of gradual moving object clusters pattern discovery from trajectory streams using sliding window models.By processing the trajectory data in current window,the mining algorithm can capture the trend and evolution of moving object clusters pattern.Firstly,the density peaks clustering algorithm is exploited to identify clusters of different snapshots.The stable relationship between relatively few moving objects is used to improve the clustering efficiency.Then,by intersecting clusters from different snapshots,the gradual moving object clusters pattern is updated.The relationship of clusters between adjacent snapshots and the gradual property are utilized to accelerate updating process.Finally,experiment results on two real datasets demonstrate that our algorithm is effective and efficient.展开更多
The graph can contain huge amount of data. It is heavily used for pattern recognition and matching tasks like symbol recognition, information retrieval, data mining etc. In all these applications, the objects or under...The graph can contain huge amount of data. It is heavily used for pattern recognition and matching tasks like symbol recognition, information retrieval, data mining etc. In all these applications, the objects or underlying data are represented in the form of graph and graph based matching is performed. The conventional algorithms of graph matching have higher complexity. This is because the most of the applications have large number of sub graphs and the matching of these sub graphs becomes computationally expensive. In this paper, we propose a graph based novel algorithm for fingerprint recognition. In our work we perform graph based clustering which reduces the computational complexity heavily. In our algorithm, we exploit structural features of the fingerprint for K-means clustering of the database. The proposed algorithm is evaluated using realtime fingerprint database and the simulation results show that our algorithm outperforms the existing algorithm for the same task.展开更多
The critical technical problem of underwater bottom object detection is founding a stable feature space for echo signals classification. The past literatures more focus on the characteristics of object echoes in featu...The critical technical problem of underwater bottom object detection is founding a stable feature space for echo signals classification. The past literatures more focus on the characteristics of object echoes in feature space and reverberation is only treated as interference. In this paper, reverberation is considered as a kind of signal with steady characteristic, and the clustering of reverberation in frequency discrete wavelet transform （FDWT） feature space is studied. In order to extract the identifying information of echo signals, feature compression and cluster analysis are adopted in this paper, and the criterion of separability between object echoes and reverberation is given. The experimental data processing results show that reverberation has steady pattern in FDWT feature space which differs from that of object echoes. It is proven that there is separability between reverberation and object echoes.展开更多
This paper proposed a new approach of sample part classification and design, a so called Or-dered-object-oriented method (O-O-O method). Based on the theory of neural networks, fuzzy clustering algorithm and adaptive ...This paper proposed a new approach of sample part classification and design, a so called Or-dered-object-oriented method (O-O-O method). Based on the theory of neural networks, fuzzy clustering algorithm and adaptive pattern recognition, O-O-O method can be used to classify and design the sample parts automatically. The basic theory, the main step as well as the characteristics of the method are analysed. The construction of the ordered object in application is also presented in this paper.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
基金Supported by the National Natural Science Foundation of China (No.50269001, 50569002, 50669004)Natural Science Foundation of Inner Mongolia (No.200208020512, 200711020604)The Key Scientific and Technologic Project of the 10th Five-Year Plan of Inner Mongolia (No.20010103)
文摘Water quality assessment of lakes is important to determine functional zones of water use.Considering the fuzziness during the partitioning process for lake water quality in an arid area,a multiplex model of fuzzy clustering with pattern recognition was developed by integrating transitive closure method,ISODATA algorithm in fuzzy clustering and fuzzy pattern recognition.The model was applied to partition the Ulansuhai Lake,a typical shallow lake in arid climate zone in the west part of Inner Mongolia,China and grade the condition of water quality divisions.The results showed that the partition well matched the real conditions of the lake,and the method has been proved accurate in the application.
文摘To avoid the complexity of building mechanistic models by studying the inner nature of the object, a systematic method based on statistical pattern recognition is developed in order to estimate the product quality on-line. The mapping relationship between a feature space and a product quality space can be built by using regression analysis, and in applying clustering analysis the product quality space can be partitioned automatically. Eventually, estimating product quality on-line can be accomplished by sorting the mapped data in the partitioned quality space. A concrete problem is proposed which has a relatively small ratio of training data to input variables. By implementing the method mentioned above, a satisfying result has been achieved. Furthermore, the further question about choosing suitable mapping methods is briefly discussed.
文摘In this paper, the characteristics of vibration signal of machinery in different running conditions are statistically analysed, and some moments of statistical distribution of signals are selected as the eigenvector to condense the state information. Here, we divide the states of machinery into two: 'good' and 'faulty', and the pattern recognition techniques are used to classify the running conditions of machinery. At the end of this paper, the authors present some test data, and from the results obtained, it's verified that the eigenvector selected is reliable and sensible to faults. And the results also show the effectiveness of classification rule.
基金supported by the National Natural Science Foundation of China(51675253)
文摘A method about fault identification is proposed to solve the relationship among fault features of large rotating machinery, which is extremely complicated and nonlinear. This paper studies the rotor test-rig and the clustering of data sets and fault pattern recognitions. The present method firstly maps the data from their original space to a high dimensional Kernel space which makes the highly nonlinear data in low-dimensional space become linearly separable in Kernel space. It highlights the differences among the features of the data set. Then fuzzy C-means （FCM） is conducted in the Kernel space. Each data is assigned to the nearest class by computing the distance to the clustering center. Finally, test set is used to judge the results. The convergence rate and clustering accuracy are better than traditional FCM. The study shows that the method is effective for the accuracy of pattern recognition on rotating machinery.
基金supported by the Centro de Investigación para el Desarrollo y la Innovación (CIDI) from Universidad Pontificia Bolivariana (No. 636B-06/16–57)。
文摘Structural Health Monitoring(SHM) suggests the use of machine learning algorithms with the aim of understanding specific behaviors in a structural system. This work introduces a pattern recognition methodology for operational condition clustering in a structure sample using the well known Density Based Spatial Clustering of Applications with Noise(DBSCAN) algorithm.The methodology was validated using a data set from an experiment with 32 Fiber Bragg Gratings bonded to an aluminum beam placed in cantilever and submitted to cyclic bending loads under 13 different operational conditions(pitch angles). Further, the computational cost and precision of the machine learning pipeline called FA + GA-DBSCAN(which employs a combination of machine learning techniques including factor analysis for dimensionality reduction and a genetic algorithm for the automatic selection of initial parameters of DBSCAN) was measured. The obtained results have shown a good performance, detecting 12 of 13 operational conditions, with an overall precision over 90%.
文摘To recognize circular objects rapidly in satellite remote sensing imagery, an approach using their geometry properties is presented. The original image is segmented to be a binary one by one dimension maximum entropy threshold algorithm and the binary image is labeled with an algorithm based on recursion technique. Then, shape parameters of all labeled regions are calculated and those regions with shape parameters satisfying certain conditions are recognized as circular objects. The algorithm is described in detail, and comparison experiments with the randomized Hough transformation （RHT） are also provided. The experimental results on synthetic images and real images show that the proposed method has the merits of fast recognition rate, high recognition efficiency and the ability of anti-noise and anti-jamming. In addition, the method performs well when some circular objects are little deformed and partly misshapen.
基金This work is supported by the National Natural Science Foundationof China under Grants No. 41471371.
文摘The discovery of gradual moving object clusters pattern from trajectory streams allows characterizing movement behavior in real time environment,which leverages new applications and services.Since the trajectory streams is rapidly evolving,continuously created and cannot be stored indefinitely in memory,the existing approaches designed on static trajectory datasets are not suitable for discovering gradual moving object clusters pattern from trajectory streams.This paper proposes a novel algorithm of gradual moving object clusters pattern discovery from trajectory streams using sliding window models.By processing the trajectory data in current window,the mining algorithm can capture the trend and evolution of moving object clusters pattern.Firstly,the density peaks clustering algorithm is exploited to identify clusters of different snapshots.The stable relationship between relatively few moving objects is used to improve the clustering efficiency.Then,by intersecting clusters from different snapshots,the gradual moving object clusters pattern is updated.The relationship of clusters between adjacent snapshots and the gradual property are utilized to accelerate updating process.Finally,experiment results on two real datasets demonstrate that our algorithm is effective and efficient.
文摘The graph can contain huge amount of data. It is heavily used for pattern recognition and matching tasks like symbol recognition, information retrieval, data mining etc. In all these applications, the objects or underlying data are represented in the form of graph and graph based matching is performed. The conventional algorithms of graph matching have higher complexity. This is because the most of the applications have large number of sub graphs and the matching of these sub graphs becomes computationally expensive. In this paper, we propose a graph based novel algorithm for fingerprint recognition. In our work we perform graph based clustering which reduces the computational complexity heavily. In our algorithm, we exploit structural features of the fingerprint for K-means clustering of the database. The proposed algorithm is evaluated using realtime fingerprint database and the simulation results show that our algorithm outperforms the existing algorithm for the same task.
基金Supported by the National Natural Science Foundation of China, under Grant No.51279033.
文摘The critical technical problem of underwater bottom object detection is founding a stable feature space for echo signals classification. The past literatures more focus on the characteristics of object echoes in feature space and reverberation is only treated as interference. In this paper, reverberation is considered as a kind of signal with steady characteristic, and the clustering of reverberation in frequency discrete wavelet transform （FDWT） feature space is studied. In order to extract the identifying information of echo signals, feature compression and cluster analysis are adopted in this paper, and the criterion of separability between object echoes and reverberation is given. The experimental data processing results show that reverberation has steady pattern in FDWT feature space which differs from that of object echoes. It is proven that there is separability between reverberation and object echoes.
基金Supported by National Natural Science Foundation of China(60675039)National High Technology Research and Development Program of China(863 Program)(2006AA04Z217)Hundred Talents Program of Chinese Academy of Sciences
文摘This paper proposed a new approach of sample part classification and design, a so called Or-dered-object-oriented method (O-O-O method). Based on the theory of neural networks, fuzzy clustering algorithm and adaptive pattern recognition, O-O-O method can be used to classify and design the sample parts automatically. The basic theory, the main step as well as the characteristics of the method are analysed. The construction of the ordered object in application is also presented in this paper.
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.
